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import gradio as gr
from openvino.inference_engine import IECore
import cv2
import numpy as np
# Load the OpenVINO model
ie = IECore()
net = ie.read_network(model='openvino/best.xml', weights='openvino/best.bin')
exec_net = ie.load_network(network=net, device_name="CPU")
# Define a function for prediction
def predict(image):
# Preprocess the image
input_blob = next(iter(net.input_info))
input_shape = net.input_info[input_blob].input_data.shape
image_resized = cv2.resize(image, (input_shape[3], input_shape[2]))
image_transposed = image_resized.transpose((2, 0, 1))
image_batch = np.expand_dims(image_transposed, axis=0)
# Perform inference
result = exec_net.infer(inputs={input_blob: image_batch})
output_blob = next(iter(net.outputs))
detections = result[output_blob]
# Post-process the results
boxes = []
for detection in detections[0][0]:
confidence = detection[2]
if confidence > 0.5: # Threshold
xmin = int(detection[3] * image.shape[1])
ymin = int(detection[4] * image.shape[0])
xmax = int(detection[5] * image.shape[1])
ymax = int(detection[6] * image.shape[0])
boxes.append((xmin, ymin, xmax, ymax, confidence))
# Draw bounding boxes on the image
for (xmin, ymin, xmax, ymax, confidence) in boxes:
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
return image
# Create a Gradio interface
iface = gr.Interface(fn=predict, inputs=gr.Image(type="numpy"), outputs=gr.Image(type="numpy"))
# Launch the Gradio app
iface.launch()
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